Sparse approximation of images inspired from the functional architecture of the primary visual areas

Abstract

Several drawbacks of critically sampled wavelets can be solved by overcomplete multiresolution transforms and sparse approximation algorithms.Facing the difficulty to optimize such nonorthogonal and nonlinear transforms, we implement a sparse approximation scheme inspired from the functional architecture of the primary visualcortex. The scheme models simple and complex cell receptive fields through log-Gabor wavelets. Themodel also incorporates inhibition and facilitation interactions between neighboring cells. Functionally these interactions allow to extract edges andridges, providing an edge-based approximation of the visual information. The edge coefficients areshown sufficient for closely reconstructing the images, while contour representations by means of chains of edges reduce the information redundancy for approaching image compression. Additionally,the ability to segregate the edges from the noise is employed for image restoration. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.